This disclosure is directed to computer generated node order fulfillment performance and more particularly, to computer generated node order fulfillment performance considering capacity utilization cost.
Omni-channel retailers employ a number of channels to fulfill online orders. One approach to find optimal fulfillment solutions is to model the fulfillment problem as a multi-objective optimization problem, where the solution is order item assignments across a large number of fulfillment candidate nodes (stores, ecommerce fulfillment centers, etc.).
A key issue when assigning a part of an order to a node for fulfillment is that the order can get backlogged due to limited node capacity, that is, the laborers who can pick the items at the node and fulfill the order. Node capacity is especially a problem when non-traditional fulfillment nodes are considered in the node fulfillment decision such as stores in the recent ship-from-store trend. On the other hand, nodes can remain underutilized—having more capacity available than is being used. Therefore, factoring in the node fulfillment capacity and capacity utilization of a node would be useful for balancing fulfillment load across retail supply networks and avoiding costly delays due to overloading the current resources of the node.